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Can Generative Colourisation Help Face Recognition?
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Datum
2020
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Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
Generative colourisation methods can be applied to automatically convert greyscale images
to realistically looking colour images. In a face recognition system, such techniques might be
employed as a pre-processing step in scenarios where either one or both face images to be compared
are only available in greyscale format. In an experimental setup which reflects said scenarios, we
investigate if generative colourisation can improve face sample utility and overall biometric performance
of face recognition. To this end, subsets of the FERET and FRGCv2 face image databases
are converted to greyscale and colourised applying two versions of the DeOldify colourisation algorithm.
Face sample quality assessment is done using the FaceQnet quality estimator. Biometric
performance measurements are conducted for the widely used ArcFace system with its built-in face
detector and reported according to standardised metrics. Obtained results indicate that, for the tested
systems, the application of generative colourisation does neither improve face image quality nor
recognition performance. However, generative colourisation was found to aid face detection and
subsequent feature extraction of the used face recognition system which results in a decrease of the
overall false reject rate.